1
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Pallauf M, Ged Y, Singla N. Molecular differences in renal cell carcinoma between males and females. World J Urol 2023; 41:1727-1739. [PMID: 36905442 DOI: 10.1007/s00345-023-04347-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 02/23/2023] [Indexed: 03/12/2023] Open
Abstract
PURPOSE The disparity in renal cell carcinoma (RCC) risk and treatment outcome between males and females is well documented, but the underlying molecular mechanisms remain poorly elucidated. METHODS We performed a narrative review synthesizing contemporary evidence on sex-specific molecular differences in healthy kidney tissue and RCC. RESULTS In healthy kidney tissue, gene expression differs significantly between males and females, including autosomal and sex-chromosome-linked genes. The differences are most prominent for sex-chromosome-linked genes and attributable to Escape from X chromosome-linked inactivation and Y chromosome loss. The frequency distribution of RCC histologies varies between the sexes, particularly for papillary, chromophobe, and translocation RCC. In clear-cell and papillary RCC, sex-specific gene expressions are pronounced, and some of these genes are amenable to pharmacotherapy. However, for many, the impact on tumorigenesis remains poorly understood. In clear-cell RCC, molecular subtypes and gene expression pathways have distinct sex-specific trends, which also apply to the expression of genes implicated in tumor progression. CONCLUSION Current evidence suggests meaningful genomic differences between male and female RCC, highlighting the need for sex-specific RCC research and personalized sex-specific treatment approaches.
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Affiliation(s)
- Maximilian Pallauf
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Park 213, Baltimore, MD, 21287, USA
- Department of Urology, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Yasser Ged
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nirmish Singla
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Park 213, Baltimore, MD, 21287, USA.
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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2
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Zabor EC, Seshan VE, Wang S, Begg CB. Validity of a method for identifying disease subtypes that are etiologically heterogeneous. Stat Methods Med Res 2021; 30:2045-2056. [PMID: 34319833 DOI: 10.1177/09622802211032704] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
A focus of cancer epidemiologic research has become the identification of risk factors that influence specific subtypes of disease, a phenomenon known as etiologic heterogeneity. In previous work we developed a novel strategy to cluster tumor markers and identify disease subtypes that differ maximally with respect to known risk factors for use in the context of case-control studies. The method relies on the premise that unsupervised k-means clustering will find candidate solutions that are closely aligned with the sought-after etiologically distinct clusters, which may not be true in the presence of clusters of tumor markers that are not related to risk of disease. In this article, we investigate in detail the ability of the method to identify the "true" clusters in the presence of clusters that are unrelated to risk factors, what we term "counterfeit" clusters. We find that our method works when the strength of structure is larger in the clusters that truly represent etiologic heterogeneity than in the counterfeit clusters, but when this condition is not met, or when there are many tumor markers that simply represent noise, the method will not find the correct solution without first performing variable selection to identify the tumor markers most strongly related to the risk factors. We illustrate the results using data from a breast cancer case-control study.
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Affiliation(s)
- Emily C Zabor
- Department of Quantitative Health Sciences & Taussig Cancer Institute, 2569Cleveland Clinic, Cleveland Clinic, Cleveland, OH, USA
| | - Venkatraman E Seshan
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shuang Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Colin B Begg
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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3
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Benefield HC, Zabor EC, Shan Y, Allott EH, Begg CB, Troester MA. Evidence for Etiologic Subtypes of Breast Cancer in the Carolina Breast Cancer Study. Cancer Epidemiol Biomarkers Prev 2019; 28:1784-1791. [PMID: 31395590 PMCID: PMC6825567 DOI: 10.1158/1055-9965.epi-19-0365] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/12/2019] [Accepted: 08/01/2019] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Distinctions in the etiology of triple-negative versus luminal breast cancer have become well established using immunohistochemical surrogates [notably estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)]. However, it is unclear whether established immunohistochemical subtypes are the sole or definitive means of etiologically subdividing breast cancers. METHODS We evaluated clinical biomarkers and tumor suppressor p53 with risk factor data from cases and controls in the Carolina Breast Cancer Study, a population-based study of incident breast cancers. For each individual marker and combinations of markers, we calculated an aggregate measure to distinguish the etiologic heterogeneity of different classification schema. To compare schema, we estimated subtype-specific case-control odds ratios for individual risk factors and fit age-at-incidence curves with two-component mixture models. We also evaluated subtype concordance of metachronous contralateral breast tumors in the California Cancer Registry. RESULTS ER was the biomarker that individually explained the greatest variability in risk factor profiles. However, further subdivision by p53 significantly increased the degree of etiologic heterogeneity. Age at diagnosis, nulliparity, and race were heterogeneously associated with ER/p53 subtypes. The ER-/p53+ subtype exhibited a similar risk factor profile and age-at-incidence distribution to the triple-negative subtype. CONCLUSIONS Clinical marker-based intrinsic subtypes have established value, yet other schema may also yield important etiologic insights. IMPACT Novel environmental or genetic risk factors may be identifiable by considering different etiologic schema, including cross-classification based on ER/p53.
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Affiliation(s)
- Halei C Benefield
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
| | - Emily C Zabor
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Emma H Allott
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom
| | - Colin B Begg
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Melissa A Troester
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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4
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Hamada T, Nowak JA, Milner DA, Song M, Ogino S. Integration of microbiology, molecular pathology, and epidemiology: a new paradigm to explore the pathogenesis of microbiome-driven neoplasms. J Pathol 2019; 247:615-628. [PMID: 30632609 PMCID: PMC6509405 DOI: 10.1002/path.5236] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/24/2018] [Accepted: 01/06/2019] [Indexed: 02/06/2023]
Abstract
Molecular pathological epidemiology (MPE) is an integrative transdisciplinary field that addresses heterogeneous effects of exogenous and endogenous factors (collectively termed 'exposures'), including microorganisms, on disease occurrence and consequences, utilising molecular pathological signatures of the disease. In parallel with the paradigm of precision medicine, findings from MPE research can provide aetiological insights into tailored strategies of disease prevention and treatment. Due to the availability of molecular pathological tests on tumours, the MPE approach has been utilised predominantly in research on cancers including breast, lung, prostate, and colorectal carcinomas. Mounting evidence indicates that the microbiome (inclusive of viruses, bacteria, fungi, and parasites) plays an important role in a variety of human diseases including neoplasms. An alteration of the microbiome may be not only a cause of neoplasia but also an informative biomarker that indicates or mediates the association of an epidemiological exposure with health conditions and outcomes. To adequately educate and train investigators in this emerging area, we herein propose the integration of microbiology into the MPE model (termed 'microbiology-MPE'), which could improve our understanding of the complex interactions of environment, tumour cells, the immune system, and microbes in the tumour microenvironment during the carcinogenic process. Using this approach, we can examine how lifestyle factors, dietary patterns, medications, environmental exposures, and germline genetics influence cancer development and progression through impacting the microbial communities in the human body. Further integration of other disciplines (e.g. pharmacology, immunology, nutrition) into microbiology-MPE would expand this developing research frontier. With the advent of high-throughput next-generation sequencing technologies, researchers now have increasing access to large-scale metagenomics as well as other omics data (e.g. genomics, epigenomics, proteomics, and metabolomics) in population-based research. The integrative field of microbiology-MPE will open new opportunities for personalised medicine and public health. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Tsuyoshi Hamada
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Jonathan A Nowak
- Department of Pathology Program in MPE Molecular Pathological Epidemiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Danny A Milner
- American Society for Clinical Pathology, Chicago, Illinois, USA
| | - Mingyang Song
- Departments of Epidemiology and Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Shuji Ogino
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
- Department of Pathology Program in MPE Molecular Pathological Epidemiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
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5
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Ogino S, Nowak JA, Hamada T, Phipps AI, Peters U, Milner DA, Giovannucci EL, Nishihara R, Giannakis M, Garrett WS, Song M. Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine. Gut 2018; 67:1168-1180. [PMID: 29437869 PMCID: PMC5943183 DOI: 10.1136/gutjnl-2017-315537] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 01/02/2018] [Accepted: 01/05/2018] [Indexed: 12/14/2022]
Abstract
Immunotherapy strategies targeting immune checkpoints such as the CTLA4 and CD274 (programmed cell death 1 ligand 1, PD-L1)/PDCD1 (programmed cell death 1, PD-1) T-cell coreceptor pathways are revolutionising oncology. The approval of pembrolizumab use for solid tumours with high-level microsatellite instability or mismatch repair deficiency by the US Food and Drug Administration highlights promise of precision immuno-oncology. However, despite evidence indicating influences of exogenous and endogenous factors such as diet, nutrients, alcohol, smoking, obesity, lifestyle, environmental exposures and microbiome on tumour-immune interactions, integrative analyses of those factors and immunity lag behind. Immune cell analyses in the tumour microenvironment have not adequately been integrated into large-scale studies. Addressing this gap, the transdisciplinary field of molecular pathological epidemiology (MPE) offers research frameworks to integrate tumour immunology into population health sciences, and link the exposures and germline genetics (eg, HLA genotypes) to tumour and immune characteristics. Multilevel research using bioinformatics, in vivo pathology and omics (genomics, epigenomics, transcriptomics, proteomics and metabolomics) technologies is possible with use of tissue, peripheral blood circulating cells, cell-free plasma, stool, sputum, urine and other body fluids. This immunology-MPE model can synergise with experimental immunology, microbiology and systems biology. GI neoplasms represent exemplary diseases for the immunology-MPE model, given rich microbiota and immune tissues of intestines, and the well-established carcinogenic role of intestinal inflammation. Proof-of-principle studies on colorectal cancer provided insights into immunomodulating effects of aspirin, vitamin D, inflammatory diets and omega-3 polyunsaturated fatty acids. The integrated immunology-MPE model can contribute to better understanding of environment-tumour-immune interactions, and effective immunoprevention and immunotherapy strategies for precision medicine.
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Affiliation(s)
- Shuji Ogino
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA,Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tsuyoshi Hamada
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA,Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA,Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Danny A Milner
- American Society for Clinical Pathology, Chicago, Illinois, USA
| | - Edward L Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Reiko Nishihara
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Marios Giannakis
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA,Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA,Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Wendy S Garrett
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA,Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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6
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Sun B, VanderWeele T, Tchetgen Tchetgen EJ. THE AUTHORS REPLY. Am J Epidemiol 2018. [PMID: 29528372 DOI: 10.1093/aje/kwy029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- BaoLuo Sun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Tyler VanderWeele
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Eric J Tchetgen Tchetgen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
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7
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Liu L, Nevo D, Nishihara R, Cao Y, Song M, Twombly TS, Chan AT, Giovannucci EL, VanderWeele TJ, Wang M, Ogino S. Utility of inverse probability weighting in molecular pathological epidemiology. Eur J Epidemiol 2017; 33:381-392. [PMID: 29264788 DOI: 10.1007/s10654-017-0346-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 12/12/2017] [Indexed: 12/17/2022]
Abstract
As one of causal inference methodologies, the inverse probability weighting (IPW) method has been utilized to address confounding and account for missing data when subjects with missing data cannot be included in a primary analysis. The transdisciplinary field of molecular pathological epidemiology (MPE) integrates molecular pathological and epidemiological methods, and takes advantages of improved understanding of pathogenesis to generate stronger biological evidence of causality and optimize strategies for precision medicine and prevention. Disease subtyping based on biomarker analysis of biospecimens is essential in MPE research. However, there are nearly always cases that lack subtype information due to the unavailability or insufficiency of biospecimens. To address this missing subtype data issue, we incorporated inverse probability weights into Cox proportional cause-specific hazards regression. The weight was inverse of the probability of biomarker data availability estimated based on a model for biomarker data availability status. The strategy was illustrated in two example studies; each assessed alcohol intake or family history of colorectal cancer in relation to the risk of developing colorectal carcinoma subtypes classified by tumor microsatellite instability (MSI) status, using a prospective cohort study, the Nurses' Health Study. Logistic regression was used to estimate the probability of MSI data availability for each cancer case with covariates of clinical features and family history of colorectal cancer. This application of IPW can reduce selection bias caused by nonrandom variation in biospecimen data availability. The integration of causal inference methods into the MPE approach will likely have substantial potentials to advance the field of epidemiology.
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Affiliation(s)
- Li Liu
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 450 Brookline Ave., Room SM1036, Boston, MA, 02215, USA.,Department of Epidemiology and Biostatistics, and the Ministry of Education Key Lab of Environment and Health, School of Public Health, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Daniel Nevo
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave., Boston, MA, 02215, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Reiko Nishihara
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 450 Brookline Ave., Room SM1036, Boston, MA, 02215, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yin Cao
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA.,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA.,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Tyler S Twombly
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA.,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tyler J VanderWeele
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave., Boston, MA, 02215, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Molin Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave., Boston, MA, 02215, USA. .,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Shuji Ogino
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA. .,Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 450 Brookline Ave., Room SM1036, Boston, MA, 02215, USA. .,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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8
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Sun B, VanderWeele T, Tchetgen Tchetgen EJ. A Multinomial Regression Approach to Model Outcome Heterogeneity. Am J Epidemiol 2017; 186:1097-1103. [PMID: 28595286 DOI: 10.1093/aje/kwx161] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Accepted: 12/05/2016] [Indexed: 11/13/2022] Open
Abstract
When a risk factor affects certain categories of a multinomial outcome but not others, outcome heterogeneity is said to be present. A standard epidemiologic approach for modeling risk factors of a categorical outcome typically entails fitting a polytomous logistic regression via maximum likelihood estimation. In this paper, we show that standard polytomous regression is ill equipped to detect outcome heterogeneity and will generally understate the degree to which such heterogeneity may be present. Specifically, nonsaturated polytomous regression will often a priori rule out the possibility of outcome heterogeneity from its parameter space. As a remedy, we propose to model each category of the outcome as a separate binary regression. For full efficiency, we propose to estimate the collection of regression parameters jointly using a constrained Bayesian approach that ensures that one remains within the multinomial model. The approach is straightforward to implement in standard software for Bayesian estimation.
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9
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Hamada T, Keum N, Nishihara R, Ogino S. Molecular pathological epidemiology: new developing frontiers of big data science to study etiologies and pathogenesis. J Gastroenterol 2017; 52:265-275. [PMID: 27738762 PMCID: PMC5325774 DOI: 10.1007/s00535-016-1272-3] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 09/22/2016] [Indexed: 02/07/2023]
Abstract
Molecular pathological epidemiology (MPE) is an integrative field that utilizes molecular pathology to incorporate interpersonal heterogeneity of a disease process into epidemiology. In each individual, the development and progression of a disease are determined by a unique combination of exogenous and endogenous factors, resulting in different molecular and pathological subtypes of the disease. Based on "the unique disease principle," the primary aim of MPE is to uncover an interactive relationship between a specific environmental exposure and disease subtypes in determining disease incidence and mortality. This MPE approach can provide etiologic and pathogenic insights, potentially contributing to precision medicine for personalized prevention and treatment. Although breast, prostate, lung, and colorectal cancers have been among the most commonly studied diseases, the MPE approach can be used to study any disease. In addition to molecular features, host immune status and microbiome profile likely affect a disease process, and thus serve as informative biomarkers. As such, further integration of several disciplines into MPE has been achieved (e.g., pharmaco-MPE, immuno-MPE, and microbial MPE), to provide novel insights into underlying etiologic mechanisms. With the advent of high-throughput sequencing technologies, available genomic and epigenomic data have expanded dramatically. The MPE approach can also provide a specific risk estimate for each disease subgroup, thereby enhancing the impact of genome-wide association studies on public health. In this article, we present recent progress of MPE, and discuss the importance of accounting for the disease heterogeneity in the era of big-data health science and precision medicine.
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Affiliation(s)
- Tsuyoshi Hamada
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave., Room SM1036, Boston, MA, 02215, USA
| | - NaNa Keum
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Reiko Nishihara
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave., Room SM1036, Boston, MA, 02215, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Shuji Ogino
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave., Room SM1036, Boston, MA, 02215, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Division of MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 450 Brookline Ave., Room SM1036, Boston, MA, 02215, USA.
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
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10
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Nishi A, Milner DA, Giovannucci EL, Nishihara R, Tan AS, Kawachi I, Ogino S. Integration of molecular pathology, epidemiology and social science for global precision medicine. Expert Rev Mol Diagn 2015; 16:11-23. [PMID: 26636627 PMCID: PMC4713314 DOI: 10.1586/14737159.2016.1115346] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The precision medicine concept and the unique disease principle imply that each patient has unique pathogenic processes resulting from heterogeneous cellular genetic and epigenetic alterations and interactions between cells (including immune cells) and exposures, including dietary, environmental, microbial and lifestyle factors. As a core method field in population health science and medicine, epidemiology is a growing scientific discipline that can analyze disease risk factors and develop statistical methodologies to maximize utilization of big data on populations and disease pathology. The evolving transdisciplinary field of molecular pathological epidemiology (MPE) can advance biomedical and health research by linking exposures to molecular pathologic signatures, enhancing causal inference and identifying potential biomarkers for clinical impact. The MPE approach can be applied to any diseases, although it has been most commonly used in neoplastic diseases (including breast, lung and colorectal cancers) because of availability of various molecular diagnostic tests. However, use of state-of-the-art genomic, epigenomic and other omic technologies and expensive drugs in modern healthcare systems increases racial, ethnic and socioeconomic disparities. To address this, we propose to integrate molecular pathology, epidemiology and social science. Social epidemiology integrates the latter two fields. The integrative social MPE model can embrace sociology, economics and precision medicine, address global health disparities and inequalities, and elucidate biological effects of social environments, behaviors and networks. We foresee advancements of molecular medicine, including molecular diagnostics, biomedical imaging and targeted therapeutics, which should benefit individuals in a global population, by means of an interdisciplinary approach of integrative MPE and social health science.
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Affiliation(s)
- Akihiro Nishi
- Yale Institute for Network Science, New Haven, CT, USA (AN); Department of Sociology, Yale University, New Haven, CT, USA (AN); Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (DAM, SO); Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA (DAM); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN, SO); Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN); Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (ELG); Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA (RN); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA (RN, AST, SO); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA (AST, IK)
| | - Danny A Milner
- Yale Institute for Network Science, New Haven, CT, USA (AN); Department of Sociology, Yale University, New Haven, CT, USA (AN); Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (DAM, SO); Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA (DAM); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN, SO); Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN); Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (ELG); Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA (RN); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA (RN, AST, SO); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA (AST, IK)
| | - Edward L. Giovannucci
- Yale Institute for Network Science, New Haven, CT, USA (AN); Department of Sociology, Yale University, New Haven, CT, USA (AN); Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (DAM, SO); Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA (DAM); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN, SO); Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN); Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (ELG); Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA (RN); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA (RN, AST, SO); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA (AST, IK)
| | - Reiko Nishihara
- Yale Institute for Network Science, New Haven, CT, USA (AN); Department of Sociology, Yale University, New Haven, CT, USA (AN); Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (DAM, SO); Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA (DAM); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN, SO); Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN); Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (ELG); Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA (RN); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA (RN, AST, SO); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA (AST, IK)
| | - Andy S. Tan
- Yale Institute for Network Science, New Haven, CT, USA (AN); Department of Sociology, Yale University, New Haven, CT, USA (AN); Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (DAM, SO); Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA (DAM); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN, SO); Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN); Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (ELG); Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA (RN); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA (RN, AST, SO); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA (AST, IK)
| | - Ichiro Kawachi
- Yale Institute for Network Science, New Haven, CT, USA (AN); Department of Sociology, Yale University, New Haven, CT, USA (AN); Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (DAM, SO); Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA (DAM); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN, SO); Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN); Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (ELG); Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA (RN); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA (RN, AST, SO); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA (AST, IK)
| | - Shuji Ogino
- Yale Institute for Network Science, New Haven, CT, USA (AN); Department of Sociology, Yale University, New Haven, CT, USA (AN); Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (DAM, SO); Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA (DAM); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN, SO); Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA (ELG, RN); Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA (ELG); Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA (RN); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA (RN, AST, SO); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA (AST, IK)
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11
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Wang M, Spiegelman D, Kuchiba A, Lochhead P, Kim S, Chan AT, Poole EM, Tamimi R, Tworoger SS, Giovannucci E, Rosner B, Ogino S. Statistical methods for studying disease subtype heterogeneity. Stat Med 2015; 35:782-800. [PMID: 26619806 DOI: 10.1002/sim.6793] [Citation(s) in RCA: 207] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Revised: 09/08/2015] [Accepted: 10/13/2015] [Indexed: 12/31/2022]
Abstract
A fundamental goal of epidemiologic research is to investigate the relationship between exposures and disease risk. Cases of the disease are often considered a single outcome and assumed to share a common etiology. However, evidence indicates that many human diseases arise and evolve through a range of heterogeneous molecular pathologic processes, influenced by diverse exposures. Pathogenic heterogeneity has been considered in various neoplasms such as colorectal, lung, prostate, and breast cancers, leukemia and lymphoma, and non-neoplastic diseases, including obesity, type II diabetes, glaucoma, stroke, cardiovascular disease, autism, and autoimmune disease. In this article, we discuss analytic options for studying disease subtype heterogeneity, emphasizing methods for evaluating whether the association of a potential risk factor with disease varies by disease subtype. Methods are described for scenarios where disease subtypes are categorical and ordinal and for cohort studies, matched and unmatched case-control studies, and case-case study designs. For illustration, we apply the methods to a molecular pathological epidemiology study of alcohol intake and colon cancer risk by tumor LINE-1 methylation subtypes. User-friendly software to implement the methods is publicly available.
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Affiliation(s)
- Molin Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Donna Spiegelman
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A
| | - Aya Kuchiba
- Department of Biostatistics, National Cancer Center, Tokyo, Japan
| | - Paul Lochhead
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, U.S.A
| | - Sehee Kim
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, U.S.A
| | - Andrew T Chan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A.,Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, U.S.A
| | - Elizabeth M Poole
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Rulla Tamimi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Shelley S Tworoger
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Edward Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Bernard Rosner
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.,Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, U.S.A
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